Human Ear Recognition by Computer 1st Edition by Bir Bhanu, Hui Chen ISBN 1848001290 9781848001299

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ISBN 10: 1848001290 
ISBN 13: 9781848001299
Author: Bir Bhanu, Hui Chen

Biometrics deal with recognition of individuals based on their physiological or behavioural characteristics. Researchers have done extensive studies on biometrics such as fingerprint, face, palm print, iris and gait. Ear, a viable new class of biometrics, has certain advantages over face and fingerprint, which are the two most common biometrics in both academic research and industrial applications. This book explores all aspects of 3D ear recognition: representation, detection, recognition, indexing and performance prediction. It uses large datasets to quantify and compare the performance of various techniques. Features and topics include: Ear detection and recognition in 2D image – 3D object recognition and 3D biometrics – 3D ear recognition – Performance comparison and prediction. The techniques discussed will be of great interest to researchers, developers and decision makers who are involved in robust human recognition by computer for a large number of practical applications.

Human Ear Recognition by Computer 1st Table of contents:

• Fundamentals of Biometric Recognition Systems
  2.1. Overview of Biometric Modalities
  2.2. How Biometric Systems Work
  2.3. Comparison of Ear Recognition with Other Biometrics (Face, Fingerprint, Iris)
  2.4. Advantages and Limitations of Ear Recognition

• Anatomy and Features of the Human Ear
  3.1. The Structure of the Human Ear
  3.2. Unique Features for Recognition: Shape, Size, and Texture
  3.3. Role of Ears in Personal Identification
  3.4. Variability in Ear Anatomy across Different Individuals

• Image Acquisition for Ear Recognition
  4.1. Image Acquisition Techniques
  4.2. High-Quality Ear Imaging for Recognition Systems
  4.3. Preprocessing of Ear Images: Noise Removal and Normalization
  4.4. Issues with Image Quality: Lighting, Angles, and Resolution

• Feature Extraction and Representation
  5.1. Techniques for Extracting Ear Features
  5.2. Geometric and Texture-Based Approaches
  5.3. Frequency and Transform-Based Methods
  5.4. Statistical and Shape Descriptors for Ear Recognition

• Ear Recognition Algorithms and Methods
  6.1. Classical Algorithms for Ear Recognition
  6.2. Machine Learning Approaches for Ear Recognition
  6.3. Deep Learning and Convolutional Neural Networks (CNNs)
  6.4. Hybrid Approaches: Combining Traditional and Modern Methods

• Matching and Classification in Ear Recognition
  7.1. Template Matching vs. Feature Matching
  7.2. Classification Techniques: K-Nearest Neighbors, SVM, Decision Trees
  7.3. Evaluation Metrics for Ear Recognition Systems
  7.4. Real-Time Matching Challenges

• Applications of Ear Recognition
  8.1. Use of Ear Recognition in Security Systems
  8.2. Ear Recognition for Forensic Applications
  8.3. Ear Recognition in Human-Computer Interaction
  8.4. Use of Ear Biometrics in Mobile Devices

• Challenges and Solutions in Ear Recognition
  9.1. Handling Variability in Ear Appearances
  9.2. Cross-Age and Cross-Viewpoint Recognition Issues
  9.3. Scalability and Real-World Deployment Challenges
  9.4. Improving Robustness and Accuracy

• Future Trends and Research Directions
  10.1. Emerging Trends in Ear Recognition Technology
  10.2. Integration of Ear Recognition with Multi-Biometrics
  10.3. Advancements in Machine Learning and AI for Ear Recognition
  10.4. Ethical and Privacy Considerations in Ear Biometrics

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